Triple

T1322757
Position Surface form Disambiguated ID Type / Status
Subject Reykjavík E28255 entity
Predicate hasTwinTown P919 FINISHED
Object Vilnius, Lithuania E105330 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Vilnius, Lithuania | Statement: [Reykjavík, hasTwinTown, Vilnius, Lithuania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vilnius, Lithuania
Context triple: [Reykjavík, hasTwinTown, Vilnius, Lithuania]
  • A. Vilnius chosen
    Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
  • B. Kaunas
    Kaunas is the second-largest city in Lithuania, known as a historic cultural and academic center located at the confluence of the Nemunas and Neris rivers.
  • C. Liepāja, Latvia
    Liepāja is a major port city on Latvia’s Baltic Sea coast, known for its historic architecture, naval heritage, and cultural life.
  • D. Klaipėda
    Klaipėda is a Lithuanian port city on the Baltic Sea known as the country’s main maritime gateway and a key regional transport and industrial hub.
  • E. Panevėžys
    Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a498540a2481909e807a762280d3ba completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c19b76b48190aa8857b80971a842 completed March 1, 2026, 10:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce621a008190a0359eaaf3241e8b completed March 8, 2026, 1:18 a.m.
Created at: March 1, 2026, 7:55 p.m.